The aim of this dissertation is to assess the effectiveness of LLMs such as FinBERT and GPT-2 in detecting fraudulent activities in financial reports and statements. This repo provides the code for implementing LLMs, traditional machine learning and deep learning models on the labelled dataset

A repository for the research article titled "DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection".

Developed and evaluated machine learning and deep learning models for detecting financial fraud.

identify fraudulent financial transactions using key behavioral patterns in transaction data. It includes comprehensive data preprocessing, model training using scikit-learn, and a Streamlit web app that allows users to input transaction details and receive instant fraud predictions.

This project aims to train a Machine Learning Algorithm to recognize financial fraud. In order to do this we use Scikit Learn.

Detection of fraudulent credit card transactions using ML

A machine learning project implementing multiple approaches to detect fraudulent credit card transactions.

Machine Learning project using PySpark & Streamlit to detect financial fraud